Focusing linear model correction and linear model correction for multivariate calibration model maintenance

ABSTRACT

A device may obtain a master beta coefficient of a master calibration model associated with a master instrument. The master beta coefficient may be at a grid of a target instrument. The device may perform constrained optimization of an objective function, in accordance with a set of constraints, in order to determine a pair of transferred beta coefficients. The constrained optimization may be performed based on an initial pair of transferred beta coefficients, the master beta coefficient, and spectra associated with a scouting set. The device may determine, based on the pair of transferred beta coefficients, a transferred beta coefficient. The device may determine a final transferred beta coefficient based on a set of transferred beta coefficients including the transferred beta coefficient. The final transferred beta coefficient may be associated with generating a transferred calibration model, corresponding to the master calibration model, for use by the target instrument.

RELATED APPLICATION

This application is a continuation of Ser. No. 17/249,572, filed Mar. 5,2021 (now U.S. Pat. No. 11,561,166), which is a continuation of U.S.patent application Ser. No. 16/032,978, filed Jul. 11, 2018 (now U.S.Pat. No. 10,969,331), the contents of each of which are incorporatedherein by reference in their entireties.

BACKGROUND

A spectroscopic instrument may be configured with a calibration modelfor calibrating spectroscopic measurements performed by thespectroscopic instrument. The calibration model is typically generatedbased on reference values, corresponding to known samples, and spectra,corresponding to the known samples, as measured by the spectroscopicinstrument.

SUMMARY

According to some possible implementations, a method may include:obtaining, by a device, a master beta coefficient of a mastercalibration model associated with a master instrument, wherein themaster beta coefficient is at a grid of a target instrument; performing,by the device, constrained optimization of an objective function, inaccordance with a set of constraints, in order to determine a pair oftransferred beta coefficients, wherein the constrained optimization isperformed based on an initial pair of transferred beta coefficients, themaster beta coefficient, and spectra associated with a scouting set;determining, by the device and based on the pair of transferred betacoefficients, a transferred beta coefficient; and determining, by thedevice, a final transferred beta coefficient based on a set oftransferred beta coefficients including the transferred betacoefficient, wherein the final transferred beta coefficient isassociated with generating a transferred calibration model,corresponding to the master calibration model, for use by the targetinstrument.

According to some possible implementations, a method may include:determining, by a device, that a grid of a master instrument, associatedwith master calibration model, does not match a grid of a targetinstrument for which a transferred calibration model, corresponding tothe master calibration model, is to be generated; interpolating, by thedevice and based on determining that the grid of the master instrumentdoes not match the grid of the target instrument, a beta coefficient ofthe master calibration model to the grid of the target instrument; anddetermining, by the device, a master beta coefficient, associated withgenerating the transferred calibration model, based on a result ofinterpolating the beta coefficient of the master calibration model tothe grid of the target instrument.

According to some possible implementations, a method may include:obtaining, by a device, a scouting set associated with updating acalibration model, wherein the scouting set includes spectra associatedwith a set of samples based on which the calibration model is to beupdated; determining, by the device, a beta coefficient associated withthe calibration model; determining, by the device and based on the betacoefficient and using a linear model correction (LMC) technique, anupdated beta coefficient associated with updating the calibration model;and updating, by the device, the calibration model based on the updatedbeta coefficient.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1C is a diagram of an overview of an example implementationdescribed herein.

FIG. 2 is a diagram of an example environment in which systems and/ormethods, described herein, may be implemented.

FIG. 3 is a diagram of example components of one or more devices of FIG.2 ;

FIG. 4 is a flow chart of an example process of a focused linear modelcorrection technique associated with determining a transferred betacoefficient for generating a transferred calibration model, as describedherein.

FIGS. 5A-5C are example diagrams associated with the focused linearmodel correction technique of FIG. 4 .

FIG. 6 is a flow chart of an example process for interpolating a betacoefficient of a master calibration model to a grid of a targetinstrument in order to determine a master beta coefficient for use witha focused linear model correction technique or a linear model correctiontechnique, as described herein.

FIGS. 7A-7C and 8A and 8B are diagrams associated with interpolating abeta coefficient of a master calibration model to a grid of a targetinstrument, and using the LMC technique and the fLMC technique,respectively, in association with performing calibration model transfer.

FIGS. 9A-9D, 10A, 10B, 11A, and 11B are diagrams illustrating exampleresults associated with achieving standardization of a calibration modelacross multiple instruments are diagrams.

FIG. 12 is a flow chart of an example process for model updating using alinear model correction technique, as described herein.

FIGS. 13A and 13B are diagrams illustrating example results ofperforming calibration model updating using a linear model correctiontechnique.

FIGS. 14A and 14B are diagrams illustrating example results associatedwith predicting reference values using a master calibration model and amaster transfer set.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

Calibration model transfer and calibration model updating are twoimportant areas in multivariate calibration model maintenance forspectroscopic applications, such as an application in the near-infrared(NIR) region.

In some cases, results are not acceptable when using a multivariatecalibration model that is developed on a first spectroscopic instrument(or under one environmental condition) in order to predict a calibratedproperty for a sample measured on a second spectroscopic instrument (orunder a different environmental condition by the first spectroscopicinstrument). Further, even for the same spectroscopic instrument, asignal may drift over time, meaning that updating an existingcalibration model would be required. When updating a calibration model,in order to avoid the cumbersome and expensive task of recollecting dataand recalibrating the existing calibration model, calibration transfertechniques can be implemented in order to transfer a calibration modelfrom one condition to another, regardless of sources of the drift.

One requirement of typical calibration model transfer techniques isacquisition of transfer data sets that include spectra from the same setof samples as collected by both a first instrument (e.g., a masterinstrument from which a calibration model is to be transferred, or agiven instrument under an original condition) and a second instrument(e.g., a target instrument to which the calibration model is to betransferred, or the given instrument under a target condition). In somecases, obtaining the transfer data sets is difficult or impossible. Forexample, when a calibration model for a perishable material needs to betransferred from a master instrument that is located in one country to atarget instrument located in another country, obtaining the transferdata sets may not be possible.

A linear model correction (LMC) technique may solve this issue byrequiring only a few spectra collected only by the target instrument.The set of spectra used by the LMC technique is referred to as ascouting set. However, for the LMC technique to work, reference values(e.g., actual values as measured in, for example, a chemistry lab) forthe scouting set are required. In some cases, obtaining these referencevalues can be quite time consuming and/or expensive.

Some implementations described herein provide a focused LMC (fLMC)technique that can be used in association with performing calibrationmodel transfer. Similar to the LMC technique, the fLMC techniquerequires only a scouting set collected by the target instrument.However, unlike the LMC technique, the fLMC technique does not requirereference values for the scouting set. As such, use of the fLMCtechnique in association with calibration model transfer reduces cost,difficulty, and/or complexity of calibration model transfer (e.g., ascompared to the LMC technique, as well as typical calibration modeltransfer techniques described above).

Further, calibration model transfer from a master instrument withcomparatively higher spectral resolution and/or a comparatively widerwavelength range to a target instrument with a comparatively lowerspectral resolution and/or a comparatively narrower wavelength range isoften encountered (e.g., when transferring a calibration model from abenchtop instrument to a portable instrument). For calibration modeltransfer in such a case, typical calibration transfer techniques requirea full master calibration set (e.g., sets of spectra, associated with aset of samples, as measured by the master instrument) in order toinitiate the calibration model transfer process. Here, spectra of themaster calibration set are interpolated to a grid of the targetinstrument, and then an intermediate model is developed for transfer tothe target instrument.

However, access to the master calibration set is not always possible.Even when the master calibration set is accessible, the master databasemay be large and/or may have a long history of maintenance, in somecases. As such, it may be difficult and/or time consuming to obtain aclean master calibration set from the database.

Some implementations described herein provide a procedure in which thefLMC technique or the LMC technique uses beta coefficients of a mastercalibration model in association with performing a calibration modeltransfer, without a need for the master calibration set. Use of the betacoefficients (rather than the master calibration set) reduces cost,difficulty, and/or complexity of the calibration model transfer.

Additionally, when a calibration model, developed on a master instrumentis to be deployed on multiple other instruments that likely haveinstrument-to-instrument variations (e.g., multiple different targetinstruments), performing calibration model transfer using a conventionalcalibration model transfer technique may be difficult (e.g., when thetarget instruments are at locations far away from the masterinstrument). In some implementations, the LMC technique or the fLMCtechnique can be configured on the multiple target instruments in orderto resolve this issue. When the master calibration model is delivered tothe target instrument, a user need only to collect a scouting a set(e.g., spectra from a few samples associated with a given application).The calibration model can be automatically corrected using these spectrain conjunction with the LMC technique (e.g., when reference values areavailable) or the fLMC technique (e.g., regardless of whether thereference values are available).

Furthermore, as described above, calibration model updating may beneeded after a calibration model is deployed on a given instrument(e.g., due to changes in samples, measurement environment, and/or thelike). A typical technique for performing calibration model updating isto add new samples to the existing calibration set and then rebuild thecalibration model. However, this technique may take a significant numberof samples to make the calibration model fit for the new samples or newconditions. Further, this technique requires all the calibration data tobe available. In addition, when the calibration database is large,particularly when the spectral range is wide and the spectral resolutionis high, rebuilding the calibration model may consume a substantialamount of time and/or resources (e.g., processor resources, batterypower, and/or the like). Thus, it may not be possible to update thecalibration model during online operation of the instrument.

Some implementations described herein provide techniques for using theLMC technique for calibration model updating. Intrinsically, the LMCtechnique requires a relatively small number of samples in order toperform calibration model updating. In some implementations, an updatingset (i.e., a scouting set associated with performing calibration modelupdating) can include samples representative of different conditions forfuture samples in order to make future prediction more accurate.Moreover, calibration model updating using the LMC technique iscomparatively faster than the typical updating technique describedabove. For example, calibration model updating using the LMC proceduremay be performed in seconds, thereby making calibration model updatingduring online operation possible.

In addition, in some cases, transfer sets from both a master instrumentand a target instrument may be available, while reference values for thetransfer sets may be unavailable. In such cases, as described here, theLMC technique can be performed using the transfer set from the targetinstrument as a scouting set, and using ref values predicted by themaster calibration model for the transfer set from the master instrumentas the reference values. Thus, the LMC technique can be performed whenspectral data are available that could otherwise be used to performother conventional calibration transfer techniques.

FIGS. 1A-1C are diagrams of example implementations described herein.FIGS. 1A and 1B are diagrams of an example implementation 100 associatedwith using a focused linear model correction (fLMC) correction techniquein association with generating a transferred calibration model, forconfiguration on a target instrument, that corresponds to a mastercalibration model associated with a master instrument.

For the purposes of example implementation 100 of FIGS. 1A and 1B, amaster calibration model, configured on a master instrument, is to betransferred to a target instrument. In other words, that a transferredcalibration model, corresponding to the master calibration modelconfigured on the master instrument, is to be generated for use by thetarget instrument. Example implementation 100 describes use a fLMCtechnique in association with generating the transferred calibrationmodel.

As shown in FIG. 1A, and by reference number 105, a modeling device(e.g., a device associated with generating the transferred calibrationmodel) may obtain a master beta coefficient of the master calibrationmodel at a grid of the target instrument.

The master beta coefficient may include a set of coefficients associatedwith the master calibration model. For example, the master betacoefficient may include a vector of regression coefficients associatedwith a partial least squares (PLS) regression calibration modelconfigured on the master instrument.

As noted above, the master beta coefficient is at the grid of the targetinstrument. The grid of the target instrument is a parameter of thetarget instrument defined by a spectral resolution and a wavelengthrange of the target instrument. Similarly, a grid of the masterinstrument is a parameter of the master instrument defined by a spectralresolution and a wavelength range of the master instrument. In someimplementations, the grid of the master instrument may be different fromthe grid of the target instrument (e.g., when the master instrument hasa comparatively higher spectral resolution and/or a wider wavelengthrange than those of the target instrument). Alternatively, the grid ofthe master instrument may match the grid of the target instrument (e.g.,when the spectral resolution and the wavelength range of the masterinstrument match those of the target instrument within a thresholdamount).

In some implementations, a manner in which the modeling device obtainsthe master beta coefficient may be based on whether the grid of themaster instrument matches the grid of the target instrument.

For example, the modeling device may determine (e.g., based oninformation provided by the master instrument and/or the targetinstrument, based on information stored or accessible by modelingdevice) whether the grid of the master instrument matches the grid ofthe target instrument. In some implementations, if the modeling devicedetermines that the grid of the master instrument matches the grid ofthe target instrument, then the modeling device may identify a betacoefficient of the master calibration model as the master betacoefficient. In other words, when the grid of the master instrumentmatches the grid of the target instrument, the modeling device maydirectly use the beta coefficient of the master instrument as the masterbeta coefficient (e.g., since the beta coefficient of the mastercalibration model is already at the grid of the target instrument). Insuch a case, the beta coefficient of the master calibration model can beused as the master beta coefficient irrespective of whether a mastercalibration set, associated with the master calibration model, isavailable.

In some implementations, if the modeling device determines that the gridof the master instrument does not match the grid of the targetinstrument, then the master instrument may obtain the master betacoefficient based on a master calibration set associated with the mastercalibration model. For example, if the grid of the master instrumentdoes not match the grid of the target instrument, then the modelingdevice may interpolate the master calibration set to the grid of thetarget instrument in order to create interpolated calibration data(i.e., spectra of the master calibration set interpolated to the grid ofthe target instrument). Here, the modeling device may generate aregression model (e.g., a PLS model, a principal component regression(PCR) model, and/or the like) based on the interpolated calibrationdata, and may determine the master beta coefficient as a betacoefficient of the regression model. In some implementations, themodeling device may obtain the master beta coefficient in this mannerwhen the master calibration set is available. For example, the modelingdevice may determine that the master calibration set is available (e.g.,accessible, not exceeding a threshold size or complexity level), and mayproceed, as described above.

In some implementations, if the modeling device determines that the gridof the master instrument does not match the grid of the targetinstrument target instrument, then the master instrument may obtain themaster beta coefficient based on a beta coefficient of the mastercalibration model, an example of which is illustrated in FIG. 1C.

FIG. 1C is a diagram of an example implementation 150 associated withinterpolating a beta coefficient the master calibration model to a gridof the target instrument in order to obtain a master beta coefficient.As shown by reference number 155, the modeling device may determine thatthe grid of the master instrument does not match the grid of the targetinstrument. As shown by reference number 160, based on thisdetermination, the modeling device may interpolate a beta coefficient ofthe master calibration model to the grid of the target instrument. Asshown by reference number 165, a result of interpolating the betacoefficient of the master calibration model to the grid of the targetinstrument may be used as the master beta coefficient. In someimplementations, the modeling device may obtain the master betacoefficient in this manner when the master calibration set isunavailable. For example, the modeling device may determine that themaster calibration set is unavailable (e.g., not accessible, exceeding athreshold size or complexity level), and may proceed as described above.

In some implementations, the modeling device may determine the masterbeta coefficient based on interpolating the beta coefficient of themaster calibration model to the grid of the target instrument inassociation with using the fLMC technique for a calibration modeltransfer (as described in association with example implementation 100).Additionally, or alternatively, a modeling device may determine themaster beta coefficient based on interpolating the beta coefficient ofthe master calibration model to the grid of the target instrument inassociation with using the LMC technique. In other words, interpolationof the beta coefficient of the master calibration model to the grid ofthe target instrument may be used in association with performing thefLMC technique or the LMC technique for calibration model transfer.

Returning to the fLMC technique associated with example implementation100, in some implementations, the modeling device may determine a finaltransferred beta coefficient based on a set of transferred betacoefficients. The final transferred beta coefficient is a betacoefficient to be used to generate the transferred calibration model. Insome implementations, the modeling device may determine each of the setof transferred beta coefficients based on a respective iteration of aconstrained optimization of an objective function, as described below.

In some implementations, for each iteration, the modeling device mayperform constrained optimization of the following objective function:

${\underset{b_{trans}}{argmin}\left( {{{X_{scout}b_{transA}} - {X_{scout}b_{transB}}}} \right)}^{2}$

with the following constraints:

corr(b _(transA) ,b _(master))≥r  (1)

corr(b _(transB) ,b _(master))≥r  (2)

slope(b _(transA) ,b _(master))≥r  (3)

slope(b _(transB) ,b _(master))≥r  (4)

minY_(cal) <<X _(scout) b _(transA)<<maxY_(cal)  (5)

minY_(cal) <<X _(scout) b _(transB)<<maxY_(cal)  (6)

where X_(scout) is the scouting set (e.g., spectra of the scouting setas measured by the target instrument), b_(transA) and b_(transB) are apair of transferred beta coefficients associated with a given iteration,b_(master) is the master beta coefficient, r is a constraint threshold,and minY_(cal) and maxY_(cal) define a calibration range associated withthe target instrument.

In some implementations, the constraint threshold r (e.g., thecorrelation constraint and/or the slope constraint, as described in theabove equations above) may be optimized using a validation set. In sucha case, a set of constraint threshold values r can be used iterativelyand an optimal r (e.g., determined based on a root mean square error ofprediction (RMSEP) of the validation set) can be used in associationwith determining a resulting transferred beta coefficient. In someimplementations, this constraint threshold optimization may be used inassociation with the fLMC technique or the LMC technique.

A reproducibility concept is introduced in order to establish theobjective function. Assuming that each of a pair of transferred betacoefficients, b_(transA) and b_(transB), can fit the scouting set, adifference in predicted values of the scouting set using b_(transA) andb_(transB) should be small. Therefore, the objective function is tominimize the squared difference in the predicted values of the scoutingset using b_(transA) and b_(transB). By using this reproducibilityconcept, the need for reference values of the scouting set is removed.In other words, due to this reproducibility concept, the fLMC techniquedoes not require reference values of the scouting set (unlike the LMCtechnique).

In order to obtain meaningful results, minimization of the objectivefunction needs to be performed under a set of constraints. For example,the set of constraints may include a correlation constraint associatedwith the master beta coefficient (b_(master)) and each of the pair oftransferred beta coefficients associated with a given iteration of theconstrained optimization of the objective function (b_(transA) andb_(transB)). According to this correlation constraint, correlationbetween b_(transA) and b_(master) and correlation between b_(transB) andb_(master) should satisfy a threshold (e.g., as indicated by equations(1) and (2), respectively, where the value r may be greater than orequal to 0.95, for example).

As another example, the set of constraints may include a slopeconstraint associated with the master beta coefficient and each of thepair of transferred beta coefficients associated with a given iterationof the constrained optimization of the object function. According tothis slope constraint, a slope between b_(transA) and b_(master) and aslope between b_(transB) and b_(master) should satisfy a threshold(e.g., as indicated by equations (3) and (4), respectively, where thevalue r may be greater than or equal to 0.95, for example).

As another example, the set of constraints may include a calibrationrange constraint for predicted values associated with the scouting set.According to this calibration constraint, the value of the scouting setas predicted using b_(transA) (i.e., X_(scout)b_(transA)) and the valueof the scouting set as predicted by b_(transB) (i.e.,X_(scout)b_(transB)) should be within a calibration range (e.g., asindicated by equations (5) and (6), respectively) or in range close toreference values of the scouting set.

In order to start a given iteration of the above constrainedoptimization procedure, initial values of b_(transA) and b_(transB) areneeded (i.e., b_(transA0) and b_(transB0), respectively). In someimplementations, the modeling device may generate the initial pair oftransferred beta coefficients based on random generation of the initialpair of transferred beta coefficients. Additionally, or alternatively,the modeling device may generate the initial pair of transferred betacoefficients based on applying a linear function, associated with arandom value, to the master beta coefficient (e.g., b_(transA0),b_(transB0)=m×b_(master)+n, where m and n are random numbers).Additionally, or alternatively, the modeling device may generate theinitial pair of transferred beta coefficients based on adding a randomvalue to the master beta coefficient (e.g., b_(transA0),b_(transB0)=b_(master)+n, where n is a random number).

For a given iteration of constrained optimization, the modeling devicemay generate a pair of initial transferred beta coefficients (e.g.,b_(transAi0) and b_(transBi0) for iteration i, and b_(transAk0) andb_(transBk0) for iteration k), and may perform constrained optimizationof the object function in order to determine a pair of transferred betacoefficients (e.g., b_(transAi) and b_(transBi) for iteration i, andb_(transAk) and b_(transBk) for iteration k). Then, the modeling devicemay then determine a transferred beta coefficient based on the pair oftransferred beta coefficients (e.g., b_(transi) for iteration i, andb_(transk) for iteration k). For example, as shown by reference number110 with respect to iteration i, the modeling device may generateb_(transAi0) and b_(transBi0), perform constrained optimization of theobject function in order to determine b_(transAi) and b_(transBi), anddetermine a transferred beta coefficient associated with iteration i(b_(transi)) based on the pair of transferred beta coefficients (e.g.,based on averaging b_(transAi) and b_(transBi)). As another example, asshown by reference number 115 with respect to iteration k, the modelingdevice may generate b_(transAk0) and b_(transBk0), perform constrainedoptimization of the object function in order to determine b_(transAk)and b_(transBk), and determine a transferred beta coefficient associatedwith iteration k (b_(transk)) based on the pair of transferred betacoefficients (e.g., based on averaging b_(transAk) and b_(transBk)).Here, b_(transi) and b_(transk) are included in the set of transferredbeta coefficients based on which the modeling device may determine thefinal transferred beta coefficient (b_(trans)).

In some implementations, the modeling device may be configured toperform multiple (e.g., 5, 20, 100, and/or the like) iterations ofconstrained optimization of the objective function (e.g., in order toavoid bias results based on the randomized nature of the initial pair oftransferred beta coefficients).

As shown in FIG. 1B, and by reference number 120, the modeling devicemay determine the final transferred beta coefficient (b_(trans)) basedon the set of transferred beta coefficients. For example, the modelingdevice may determine the final transferred beta coefficient as beingequal to a mean, a median, a mode, and/or the like, of the set oftransferred beta coefficients (e.g., b_(transi) through b_(transk)).

As shown by reference number 125, the modeling device may generate thetransferred calibration model based on the final transferred betacoefficient. For example, the modeling device may generate a regressionmodel (e.g., a PLS model, a PCR model, and/or the like) based on thefinal transferred beta coefficient. As shown by reference number 130,the modeling device may provide the transferred calibration model to thetarget instrument (e.g., such that the target instrument can use thetransferred calibration model). In this way, the modeling device may beconfigured to use a fLMC technique that allows the modeling device togenerate a transferred calibration model using spectra associated with ascouting set, without a need for reference values of the scouting set.

As indicated above, FIGS. 1A-1C are provided merely as examples. Otherexamples are possible and may differ from what was described with regardto FIGS. 1A-1C.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods, described herein, may be implemented. As shown in FIG. 2, environment 200 may include a master instrument 205, a targetinstrument 210, a modeling device 215, and a network 220. Devices ofenvironment 200 may interconnect via wired connections, wirelessconnections, or a combination of wired and wireless connections.

Master instrument 205 includes a device, configured with a mastercalibration model, that is capable of performing a spectroscopicmeasurement on a sample. For example, master instrument 205 may includea desktop (i.e., non-handheld) spectrometer device that performsspectroscopy (e.g., vibrational spectroscopy, such as near infrared(NIR) spectroscopy, mid-infrared spectroscopy (mid-IR), Ramanspectroscopy, or the like). In some implementations, master instrument205 may be capable of obtaining spectroscopic measurements at a higherresolution than spectroscopic measurements obtained by target instrument210 (i.e., master instrument 205 may be a high-resolution device, whiletarget instrument 210 may be a low-resolution device). For example,master instrument 205 may be capable of obtaining spectroscopicmeasurements on 400 channels, while target instrument 210 may be capableof obtaining spectroscopic measurement on 125 channels. In someimplementations, master instrument 205 may be configured with a mastercalibration model for calibrating spectroscopic measurements obtained bymaster instrument 205. In some implementations, master instrument 205may receive information from and/or transmit information to anotherdevice in environment 200, such as modeling device 215.

Target instrument 210 includes a device capable of performing aspectroscopic measurement on a sample based on a target calibrationmodel, where the target calibration model may be generated based oninformation associated with a master calibration model associated withmaster instrument 205, as described herein. For example, targetinstrument 210 may include a mobile spectrometer device or a handheldspectrometer device that performs spectroscopy. In some implementations,target instrument 210 may be capable of obtaining spectroscopicmeasurements at a lower resolution than spectroscopic measurementsobtained by master instrument 205. In some implementations, targetinstrument 210 may receive information from and/or transmit informationto another device in environment 200, such as modeling device 215.

Modeling device 215 includes a device capable of performing operationsassociated with transferring a master calibration model from masterinstrument 205 to target instrument 210 (i.e., generating a transferredcalibration model corresponding to the master calibration model) and/orupdating a calibration model configured on a given instrument (e.g.,master instrument 205 or target instrument 210) as described herein. Forexample, modeling device 215 may include a server, a group of servers, acomputer, a cloud computing device, or the like. In someimplementations, modeling device 215 may receive information from and/ortransmit information to another device in environment 200, such asmaster instrument 205 and/or target instrument 210. In someimplementations, modeling device 215 and master instrument 205 may beimplemented within a single device. Alternatively, modeling device 215and target instrument 210 may be implemented within a single device, insome implementations.

Network 220 includes one or more wired and/or wireless networks. Forexample, network 220 may include a cellular network (e.g., a New Radio(NR/5G) network, a long-term evolution (LTE) network, a 3G network, acode division multiple access (CDMA) network, etc.), a public landmobile network (PLMN), a local area network (LAN), a wide area network(WAN), a metropolitan area network (MAN), a telephone network (e.g., thePublic Switched Telephone Network (PSTN)), a private network, an ad hocnetwork, an intranet, the Internet, a fiber optic-based network, a cloudcomputing network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 2 .

Furthermore, two or more devices shown in FIG. 2 may be implementedwithin a single device, or a single device shown in FIG. 2 may beimplemented as multiple, distributed devices. For example, althoughmaster instrument 205 and modeling device 215 are described as being twoseparate devices, master instrument 205 and modeling device 215 may beimplemented within a single device. As another example, targetinstrument 210 and modeling device 215 may be implemented within asingle device. Additionally, or alternatively, a set of devices (e.g.,one or more devices) of environment 200 may perform one or morefunctions described as being performed by another set of devices ofenvironment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to master instrument 205, target instrument 210, and/ormodeling device 215. In some implementations, master instrument 205,target instrument 210, and/or modeling device 215 may include one ormore devices 300 and/or one or more components of device 300. As shownin FIG. 3 , device 300 may include a bus 310, a processor 320, a memory330, a storage component 340, an input component 350, an outputcomponent 360, and a communication interface 370.

Bus 310 includes a component that permits communication among thecomponents of device 300. Processor 320 is implemented in hardware,firmware, or a combination of hardware and software. Processor 320 takesthe form of a central processing unit (CPU), a graphics processing unit(GPU), an accelerated processing unit (APU), a microprocessor, amicrocontroller, a field-programmable gate array (FPGA), anapplication-specific integrated circuit (ASIC), or another type ofprocessing component. In some implementations, processor 320 includesone or more processors capable of being programmed to perform afunction. Memory 330 includes a random access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, amagneto-optic disk, and/or a solid state disk), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a sensor for sensing information (e.g., a global positioningsystem (GPS) component, an accelerometer, a gyroscope, and/or anactuator). Output component 360 includes a component that providesoutput information from device 300 (e.g., a display, a speaker, and/orone or more light-emitting diodes (LEDs)).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver and/or a separate receiver and transmitter) that enablesdevice 300 to communicate with other devices, such as via a wiredconnection, a wireless connection, or a combination of wired andwireless connections. Communication interface 370 may permit device 300to receive information from another device and/or provide information toanother device. For example, communication interface 370 may include anEthernet interface, an optical interface, a coaxial interface, aninfrared interface, a radio frequency (RF) interface, a universal serialbus (USB) interface, a Wi-Fi interface, a cellular network interface, orthe like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. Acomputer-readable medium is defined herein as a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardwired circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3 . Additionally, or alternatively,a set of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 of a focused linearmodel correction (fLMC) technique associated with determining atransferred beta coefficient for generating a transferred calibrationmodel, as described herein. In some implementations, one or more processblocks of FIG. 4 may be performed by modeling device 215. In someimplementations, one or more process blocks of FIG. 4 may be performedby another device or a group of devices separate from or includingmodeling device 215, such as master instrument 205 and/or targetinstrument 210.

As shown in FIG. 4 , process 400 may include obtaining a master betacoefficient of a master calibration model associated with a masterinstrument, wherein the master beta coefficient is at a grid of a targetinstrument (block 410). For example, modeling device 215 may obtain amaster beta coefficient of a master calibration model associated withmaster instrument 205, wherein the master beta coefficient is at a gridof target instrument 210, as described above.

As further shown in FIG. 4 , process 400 may include performingconstrained optimization of an objective function, in accordance with aset of constraints, in order to determine a pair of transferred betacoefficients, wherein the constrained optimization is performed based onan initial pair of transferred beta coefficients, the master betacoefficient, and spectra associated with a scouting set (block 420). Forexample, modeling device 215 may perform constrained optimization of anobjective function, in accordance with a set of constraints, in order todetermine a pair of transferred beta coefficients, wherein theconstrained optimization is performed based on an initial pair oftransferred beta coefficients, the master beta coefficient, and spectraassociated with a scouting set, as described above.

As further shown in FIG. 4 , process 400 may include determining, basedon the pair of transferred beta coefficients, a transferred betacoefficient (block 430). For example, modeling device 215 may determine,based on the pair of transferred beta coefficients, a transferred betacoefficient, as described above.

As further shown in FIG. 4 , process 400 may include determining a finaltransferred beta coefficient based on a set of transferred betacoefficients including the transferred beta coefficient, wherein thefinal transferred beta coefficient is associated with generating atransferred calibration model, corresponding to the master calibrationmodel, for use by the target instrument (block 440). For example,modeling device 215 may determine a final transferred beta coefficientbased on a set of transferred beta coefficients including thetransferred beta coefficient, wherein the final transferred betacoefficient is associated with generating a transferred calibrationmodel, corresponding to the master calibration model, for use by targetinstrument 210.

Process 400 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In some implementations, modeling device 215 and/or target instrument210 may generate the transferred calibration model based on the finaltransferred beta coefficient.

In some implementations, the set of transferred beta coefficientsincludes at least one other transferred beta coefficient, each beingdetermined based on a respective performance of constrained optimizationof the objective function based on respective initial pairs oftransferred beta coefficients.

In some implementations, when obtaining the master beta coefficient,modeling device 215 may determine that a grid of master instrument 205matches the grid of target instrument 210, and identify the betacoefficient of the master calibration model as the master betacoefficient.

In some implementations, when obtaining the master beta coefficient,modeling device 215 may determine that a grid of master instrument 205does not match the grid of target instrument 210; interpolate, based ondetermining that the grid of master instrument 205 does not match thegrid of target instrument 210, a master calibration set to the grid oftarget instrument 210 in order to create interpolated calibration data;generate a regression model based on the interpolated calibration data;and determine the master beta coefficient as a beta coefficient of theregression model.

In some implementations, when obtaining the master beta coefficient,modeling device 215 may determine that a grid of master instrument 205does not match the grid of target instrument 210; interpolate a betacoefficient of the master calibration model to the grid of targetinstrument 210 based on determining that the grid of master instrument205 does not match the grid of target instrument 210; and determine themaster beta coefficient based on a result of interpolating the betacoefficient of the master calibration model to the grid of targetinstrument 210. In some implementations, the beta coefficient of themaster calibration model is interpolated to the grid of targetinstrument 210 based on a determination that a master calibration set,associated with the master calibration model, is unavailable.

In some implementations, the set of constraints includes a correlationconstraint associated with the master beta coefficient and each of thepair of transferred beta coefficients, and/or a slope constraintassociated with the master beta coefficient and each of the pair oftransferred beta coefficients, in addition to a calibration rangeconstraint for predicted values associated with the scouting set.

In some implementations, modeling device 215 may generate the initialpair of transferred beta coefficients based on random generation of theinitial pair of transferred beta coefficients, applying a linearfunction, associated with a random value, to the master betacoefficient, and/or adding a random value to the master betacoefficient.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4 . Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

In order to illustrate the effectiveness of the fLMC technique, a PLSregression model for Brix of sugarcane was transferred from a benchtopFOSS NIR master instrument to a portable MicroNIR target instrument.FIGS. 5A-5C are diagrams associated with a result of this examplecalibration model transfer using the fLMC technique.

In total, 1712 FOSS spectra were used to build the master calibrationmodel. These spectra were first interpolated to the MicroNIR grid. Anintermediate master calibration model was built using these interpolatedcalibration data and the resulted beta coefficients were used asb_(master). There were 126 spectra collected by the MicroNIR instrument,out of which 15 spectra were randomly selected as the scouting set toperform fLMC. The rest of the 111 spectra were used as an externalvalidation set to validate the transferred calibration model. Predictionperformance of the transferred calibration model was compared with thatof the master calibration model using FOSS validation set from the same111 samples.

As shown in FIG. 5A, without performing calibration model transfer, aroot mean square error for prediction (RMSEP) was high when using theintermediate master calibration model to predict the MicroNIR validationset. However, when calibration model transfer was performed using thefLMC technique, RMSEP was significantly reduced, as shown in FIG. 5B.The RMSEP using the original FOSS model for the FOSS validation set wasalso calculated and used as a benchmark to evaluate performance of thetransferred calibration model. In FIG. 5C, it can be seen that residualsbetween the predicted Brix values and the lab Brix values for thevalidation set by the transferred calibration model stayed withinapproximately ±2RMSEP of the original FOSS master calibration model,indicating approximately 95% confidence that the transferred MicroNIRcalibration lies within the original bounds of the FOSS calibration.These results indicate that performance of the transferred calibrationmodel, generated using the fLMC technique, is close to the original FOSSmaster calibration model (e.g., that has a comparatively widerwavelength range and a comparatively higher spectral resolution).

In addition, for comparison, the same FOSS master calibration model wastransferred to MicroNIR using a mean difference correction (MDC)technique and piecewise direct standardization (PDS) technique, whichare two typical techniques for calibration model transfer. In order toapply these two techniques, transfer sets consisting of 15 spectra fromboth the master instrument and the target instrument were used. Thesespectra were from the same samples as used in the scouting set whenusing the fLMC technique. RMSEP for the same validation set was 1.80 and0.72 using the transferred calibration models by MDC and PDS,respectively. Thus, the fLMC technique performed better than the MDCtechnique and worse than the PDS technique in this case. However, unlikethe PDS technique, the fLMC technique does not require the transfer seton the master instrument, thereby making the fLMC techniquecomparatively less costly and/or complex, while achieving similarperformance.

As indicated above, FIGS. 5A-5C are provided merely as examples. Otherexamples are possible and may differ from what was described with regardto FIGS. 5A-5C.

FIG. 6 is a flow chart of an example process 600 for interpolating abeta coefficient of a master calibration model to a grid of a targetinstrument in order to determine a master beta coefficient for use witha fLMC technique or a LMC technique. In some implementations, one ormore process blocks of FIG. 6 may be performed by modeling device 215.In some implementations, one or more process blocks of FIG. 6 may beperformed by another device or a group of devices separate from orincluding modeling device 215, such as master instrument 205 and/ortarget instrument 210.

As shown in FIG. 6 , process 600 may include determining that a grid ofa master instrument, associated with master calibration model, does notmatch a grid of a target instrument for which a transferred calibrationmodel, corresponding to the master calibration model, is to be generated(block 610). For example, modeling device 215 may determine that a gridof master instrument 205, associated with master calibration model, doesnot match a grid of target instrument 210 for which a transferredcalibration model, corresponding to the master calibration model, is tobe generated, as described above.

As further shown in FIG. 6 , process 600 may include interpolating,based on determining that the grid of the master instrument does notmatch the grid of the target instrument, a beta coefficient of themaster calibration model to the grid of the target instrument (block620). For example, modeling device 215 may interpolate, based ondetermining that the grid of master instrument 205 does not match thegrid of target instrument 210, a beta coefficient of the mastercalibration model to the grid of target instrument 210, as describedabove.

As further shown in FIG. 6 , process 600 may include determining amaster beta coefficient, associated with generating the transferredcalibration model, based on a result of interpolating the betacoefficient of the master calibration model to the grid of the targetinstrument (block 630). For example, modeling device 215 may determine amaster beta coefficient, associated with generating the transferredcalibration model, based on a result of interpolating the betacoefficient of the master calibration model to the grid of targetinstrument 210, as described above.

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In some implementations, the beta coefficient of the master calibrationmodel is interpolated to the grid of target instrument 210 based on adetermination that a master calibration set, associated with the mastercalibration model, is unavailable.

In some implementations, modeling device 215 may perform constrainedoptimization of an objective function, in accordance with a set ofconstraints, in order to determine a pair of transferred betacoefficients, wherein the constrained optimization is performed based onan initial pair of transferred beta coefficients, the master betacoefficient, and spectra associated with a scouting set. Here, modelingdevice may determine, based on the pair of transferred betacoefficients, a transferred beta coefficient; may determine a finaltransferred beta coefficient based on a set of transferred betacoefficients including the transferred beta coefficient. In other words,in some implementations, modeling device 215 may determine the finaltransferred beta coefficient using a fLMC technique. In someimplementations, the set of constraints includes a correlationconstraint associated with the master beta coefficient and each of thepair of transferred beta coefficients, a slope constraint associatedwith the master beta coefficient and each of the pair of transferredbeta coefficients, and a calibration range constraint for predictedvalues associated with the scouting set. In some implementations,modeling device 215 may generate the initial pair of transferred betacoefficients based on random generation of the initial pair oftransferred beta coefficients, applying a linear function, associatedwith a random value, to the master beta coefficient, or adding a randomvalue to the master beta coefficient

In some implementations, modeling device 215 may determine, based on themaster beta coefficient and using a linear model correction (LMC)technique, a transferred beta coefficient associated with generating thetransferred calibration model. In other words, in some implementations,modeling device 215 may determine the final transferred beta coefficientusing a LMC technique. In some implementations, reference values for ascouting set, associated with using the LMC technique, are predictedbased on the master calibration model and a master transfer set.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6 . Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

In some implementations, a beta coefficient of a master calibrationmodel can be interpolated to a grid of target instrument 210 and used asthe master beta coefficient, as described above. For example, in someimplementations, this technique can be used in conjunction with the LMCtechnique or the fLMC technique. FIGS. 7A-7C and FIGS. 8A and 8B arediagrams associated with interpolating a beta coefficient of a mastercalibration model to a grid of a target instrument, and using the LMCtechnique and the fLMC technique, respectively, in association withperforming calibration model transfer.

Using the same data sets as described above with regard to FIGS. 5A-5C,the LMC technique was performed using a result of interpolating a betacoefficient of a master calibration model to a grid of target instrumentas a master beta coefficient, the results of which are shown in FIGS.7A-7C. Here, since there is no master calibration set available, it isnot possible to build an intermediate master calibration model. As shownin FIG. 7A, when directly using the interpolated beta coefficients topredict the validation set on the target instrument, the resulted RMSEPwas high. As shown in FIG. 7B, when using the interpolated betacoefficients as the master beta coefficient and performing the LMCtechnique, the RMSEP was significantly reduced. Further, as shown inFIG. 7C, residuals between the predicted Brix values and the lab Brixvalues for the validation set by the transferred calibration modelstayed within ±2RMSEP of the original FOSS master calibration model,with limited exceptions.

Further, using the same data sets as described above with regard toFIGS. 5A-5C, the fLMC technique was performed using a result ofinterpolating the beta coefficient of the master calibration model tothe grid of the target instrument as the master beta coefficient, theresults are shown in FIGS. 8A and 8B. Although performance was slightlyreduced as compared to using the LMC technique, the RMSEP wassignificantly reduced compared to that without performing calibrationmodel transfer (as shown in FIG. 5A). As shown in FIG. 8A, the RMSEP wasreasonably low with a normalized RMSEP of 7.7% (normalized to the meanBrix value of the validation set). Further, as shown in FIG. 8B, amajority of the residuals between the predicted Brix values and the labBrix values for the validation set by the transferred calibration modelstayed within ±2RMSEP of the original FOSS master calibration model.Notably, the fLMC technique is the only technique that can be used in acase where master calibration set is unavailable, grids differ betweenthe master instrument and the target instrument, only a scouting setcollected by the target instrument for transfer, and there are noreference values for the scouting set. In some implementations, theperformance of the transferred calibration model may be further improvedwith calibration model updating.

As indicated above, FIGS. 7A-7C and 8A and 8B are provided merely asexamples. Other examples are possible and may differ from what wasdescribed with regard to FIGS. 7A-7C and FIGS. 8A and 8B.

In some implementations, the techniques described herein may be used inorder to achieve standardization of calibration models across multipleinstruments. As described above, instrument-to-instrument variations arecommonly encountered for the same type of instruments or devices. Thus,when a calibration model is developed on one instrument but needs to bedeployed on multiple (e.g., hundreds, millions, and/or the like) ofinstruments, instrument-to-instrument variations may cause inconsistentperformance. It may not be practical to perform calibration modeltransfer using typical methods for this problem, especially when theinstruments are at various locations. The LMC technique and the fLMCtechnique can be configured on the instruments in order to solve thisproblem. Here, when a master calibration model is delivered to a targetinstrument, spectra from only a few samples need to be collected. Thecalibration model can be corrected automatically using the LMC technique(e.g., when reference values for the scouting set are available) or thefLMC technique (e.g., regardless of whether reference values for thescouting set are available).

FIGS. 9A-9D, 10A, 10B, 11A, and 11B are diagrams illustrating exampleresults associated with achieving standardization of a calibration modelacross multiple instruments. In the example associated with FIGS. 9A-9D,10A, 10B, 11A, and 11B, raw data from a MicroNIR device were calibratedin two different ways (Data A and Data B) to simulateinstrument-to-instrument variations. 759 spectra from 38 mixture sampleswere used to build a calibration model to predict caffeine content. 200spectra from the other 10 mixture samples were used as a validation set.As shown in FIGS. 9A and 9B, when using calibration model A to predictvalidation data A, or when using calibration model B to predictvalidation data B, the performance was similar. However, as shown inFIG. 9C, when using calibration model A to predict validation data B,the performance was deteriorated. As shown in FIG. 9D, many residualsbetween the predicted values and the lab values for the validation set Bwere out of the ±2RMSEP benchmarks of using model A to predictvalidation A.

Ten samples with three replicated spectra were randomly selected fromthe calibration set B as the scouting set to perform the LMC techniqueand the fLMC technique. As shown in FIG. 10A, with the LMC technique,RMSEP was significantly reduced. As shown in FIG. 10B, all theprediction residuals were within the ±2RMSEP benchmarks of using model Ato predict validation A. As shown in FIG. 11A, with the fLMC technique,RMSEP was similarly reduced. As shown in FIG. 11B, most of theprediction residuals were within the ±2RMSEP benchmarks of using model Ato predict validation A. Hence, it is effective to use the LMC techniqueor the fLMC technique to correct for instrument-to-instrumentdifferences in model performance.

In fact, when as few as 8 samples were used as the scouting set, the LMCtechnique and the fLMC technique are effective. Notably, the results inFIGS. 10A, 10B, 11A and 11B are examples of medium performance. Thescouting samples were selected randomly to simulate the real testingscenario on the user side. Final performance results were impacted bywhich samples were used as the scouting set. Again, the fLMC techniquedid not perform as well as the LMC technique. However, when there are noreference values available for the scouting set, the fLMC technique isthe only technique that can be used for calibration model transfer.

As indicated above, FIGS. 9A-9D, 10A, 10B, 11A, and 11B are providedmerely as examples. Other examples are possible and may differ from whatwas described with regard to FIGS. 9A-9D, 10A, 10B, 11A, and 11B.

As described above, in some cases, the LMC technique can be applied tocalibration model updating by using updating samples as a scouting setfor the LMC technique. Notably, this does not require the use of all thecalibration data (e.g., as required by a typical model updatingtechnique that adds the updating samples to the calibration set andrecalibrates the model), and takes a relatively short amount time suchthat calibration model updating can be performed during online operationof an instrument (e.g., master instrument 205, target instrument 210).

FIG. 12 is a flow chart of an example process 1200 for using the LMCtechnique in order to perform calibration model updating. In someimplementations, one or more process blocks of FIG. 12 may be performedby modeling device 215. In some implementations, one or more processblocks of FIG. 12 may be performed by another device or a group ofdevices separate from or including modeling device 215, such as masterinstrument 205 and/or target instrument 210.

As shown in FIG. 12 , process 1200 may include obtaining a scouting setassociated with updating a calibration model, wherein the scouting setincludes spectra associated with a set of samples based on which thecalibration model is to be updated (block 1210). For example, modelingdevice 215 may obtain a scouting set associated with updating acalibration model, wherein the scouting set includes spectra associatedwith a set of samples based on which the calibration model is to beupdated.

As further shown in FIG. 12 , process 1200 may determining, a betacoefficient associated with the calibration model (block 1220). Forexample, modeling device 215 may determine a beta coefficient associatedwith the calibration model.

As further shown in FIG. 12 , process 1200 may include determining,based on the beta coefficient and using a LMC technique, an updated betacoefficient associated with updating the calibration model (block 1230).For example, modeling device 215 may determine, based on the betacoefficient and using a LMC technique, an updated beta coefficientassociated with updating the calibration model.

As further shown in FIG. 12 , process 1200 may include updating thecalibration model based on the updated beta coefficient (block 1240).For example, modeling device 215 may update the calibration model basedon the updated beta coefficient (e.g., such that the updated calibrationmodel uses the updated beta coefficient in association with performingcalibration).

Process 1200 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In some implementations, the updating of the calibration model isperformed during operation of the instrument (e.g., master instrument205, target instrument 210) without taking the device offline.

Although FIG. 12 shows example blocks of process 1200, in someimplementations, process 1200 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 12 . Additionally, or alternatively, two or more of theblocks of process 1200 may be performed in parallel.

FIGS. 13A and 13B are diagrams illustrating example results ofperforming calibration model updating using a linear model correctiontechnique.

In order to update the Brix model (described above in association withFIG. 7B) for sugarcane, an additional 30 MicroNIR spectra were used asthe updating set. Here, the LMC technique was applied to update thecalibration model. FIG. 13A is a diagram that shows prediction resultsassociated with this update, for the same validation set as used in FIG.7B. As shown, the model performance was improved, with reduced RMSEP andreduced prediction residuals, as shown in FIG. 13B.

As indicated above, FIGS. 13A and 13B are provided merely as examples.Other examples are possible and may differ from what was described withregard to FIGS. 13A and 13B.

As described above, the LMC technique requires reference values for thescouting set. When transfer sets from both master instrument 205 andtarget instrument 210 are available, but reference values for thesesamples are unavailable, the reference values can be predicted using amaster calibration model and a master transfer set in order to make theLMC technique usable.

FIGS. 14A and 14B are diagrams illustrating example results associatedwith predicting reference values using a master calibration model and amaster transfer set. As shown in FIG. 14A, using the same data sets asthose associated with FIGS. 5A-5C, RMSEP was 0.44 when the truereference values were used. As shown by FIG. 14B, the RMSEP was 0.80when using reference values predicted using the master calibration modeland the master transfer set. Although performance of the LMC techniqueperformance using the predicted reference values was not as good asperforming the LMC technique using the true reference values, theperformance was improved as compared to using the MDC technique or thefLMC technique, and similar to using the PDS technique, as describedabove.

As indicated above, FIGS. 14A and 14B are provided merely as examples.Other examples are possible and may differ from what was described withregard to FIGS. 14A and 14B.

Some implementations described herein provide a focused LMC (fLMC)technique that can be used in association with performing calibrationmodel transfer. Similar to the LMC technique, the fLMC techniquerequires only a scouting set collected by the target instrument.However, unlike the LMC technique, the fLMC technique does not requirereference values for the scouting set. As such, use of the fLMCtechnique in association with calibration model transfer reduces cost,difficulty, and/or complexity of calibration model transfer (e.g., ascompared to the LMC technique, as well as typical calibration modeltransfer techniques described above).

Some implementations described herein provide a procedure in which thefLMC technique or the LMC technique uses beta coefficients of a mastercalibration model in association with performing a calibration modeltransfer, without a need for the master calibration set.

Some implementations described herein provide a procedure for modelupdating using the LMC technique.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations are possible inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term component is intended to be broadly construedas hardware, firmware, and/or a combination of hardware and software.

Some implementations are described herein in connection with thresholds.As used herein, satisfying a threshold may refer to a value beinggreater than the threshold, more than the threshold, higher than thethreshold, greater than or equal to the threshold, less than thethreshold, fewer than the threshold, lower than the threshold, less thanor equal to the threshold, equal to the threshold, or the like.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwarecan be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of possible implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of possible implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related items,and unrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A method, comprising: receiving, by a device andafter a calibration model is deployed to a spectroscopic instrument, anupdating set that includes new samples representative of differentconditions; and updating, by the device and after the calibration modelis deployed to the spectroscopic instrument, the calibration model usinga linear model correction (LMC) technique and the updating set.
 2. Themethod of claim 1, wherein the calibration model is updated withoutusing all calibration data of an existing calibration set.
 3. The methodof claim 1, wherein the calibration model is updated without adding thenew samples to an existing calibration set.
 4. The method of claim 1,wherein the updating set is a scouting set associated with performingcalibration model updating.
 5. The method of claim 1, wherein thecalibration model is updated during operation of the spectroscopicinstrument.
 6. The method of claim 1, further comprising: receiving amaster calibration model from a master instrument; generating atransferred calibration model corresponding to a master calibrationmodel; and transmitting the transferred calibration model to a targetinstrument, wherein the spectroscopic instrument is the masterinstrument or the target instrument, and wherein the calibration modelis the master calibration model or the transferred calibration model. 7.The method of claim 1, wherein the spectroscopic instrument isimplemented within the device.
 8. The method of claim 1, furthercomprising: determining, by the device, a beta coefficient associatedwith the calibration model, wherein the calibration model is updatedfurther based on the beta coefficient.
 9. The method of claim 1, whereinupdating the calibration model comprises: determining, using the LMCtechnique and the updating set, an updated beta coefficient associatedwith updating the calibration model; and updating, by the device, thecalibration model based on the updated beta coefficient.
 10. A device,comprising: one or more memories; and one or more processors, coupled tothe one or more memories, configured to: receive, after a calibrationmodel is deployed to a spectroscopic instrument, an updating set thatincludes new samples representative of different conditions; and update,after the calibration model is deployed to the spectroscopic instrument,the calibration model using a linear model correction (LMC) techniqueand the updating set.
 11. The device of claim 10, wherein thecalibration model is updated without using all calibration data of anexisting calibration set.
 12. The device of claim 10, wherein thecalibration model is updated without adding the new samples to anexisting calibration set.
 13. The device of claim 10, wherein theupdating set is a scouting set associated with performing calibrationmodel updating.
 14. The device of claim 10, wherein the calibrationmodel is updated without taking the spectroscopic instrument offline.15. The device of claim 10, wherein the spectroscopic instrument is atarget instrument, and wherein the calibration model is a transferredcalibration model.
 16. The device of claim 10, wherein the spectroscopicinstrument is implemented within the device.
 17. A non-transitorycomputer-readable medium storing a set of instructions, the set ofinstructions comprising: one or more instructions that, when executed byone or more processors of a device, cause the device to: receive, aftera calibration model is deployed to a spectroscopic instrument, anupdating set that includes new samples representative of differentconditions; and update, after the calibration model is deployed to thespectroscopic instrument, the calibration model using a linear modelcorrection (LMC) technique and the updating set.
 18. The non-transitorycomputer-readable medium of claim 17, wherein the calibration model isupdated without using all calibration data of an existing calibrationset and without adding the new samples to the existing calibration set.19. The non-transitory computer-readable medium of claim 17, wherein thecalibration model is updated during operation of the spectroscopicinstrument.
 20. The non-transitory computer-readable medium of claim 19,wherein the spectroscopic instrument is a master instrument, and whereinthe calibration model is a master calibration model.